When doing business with humans

Customers don’t care about your shiny new tech, only about what improves their experience with a brand.  That’s the thing to remember when setting up communication channels: tech solutions are not necessarily going to make your customers happier, and they can have the opposite effect. 

That was the subject of a recent survey of over 2,000 U.S. consumers commissioned by Invoca. 

Among its findings was that over half of customers (52 percent) report feeling frustrated with companies that offer “no option for a human interaction” and close to a fifth (18 percent) go so far as to express anger about it. That indicates a setup that relies exclusively on automated communication incurs more negative than positive feeling, as only 16 percent report that they enjoy the experience. 

It’s not a big surprise that Invoca finds variations between age groups with respect to trust in AI. Generally, the younger they are, the more they trust. “AI generated advice (80 percent of 18 to 34-year-olds vs. 62 percent ages 35 plus) — especially when it comes to financial services and healthcare, with 22 percent (each) of 18 to 34-year-olds trusting AI-generated advice for healthcare and finance compared to 10 percent (each) of 65-plus year olds.” 

Read more in Too Much AI? 

Related: 

Customers and brands are not on the same page about tech

Bard brings in the clowns for my query on ICP

 

I've been reading Built to Last, a 1994 book about  successful businesses. None of the business models relied on the internet, and many of them already had many decades of history behind them. I noticed that the jargon in the book is quite different from today's.

For example, there is not a single mention of ICP (ideal customer profile). That made me curious. Did this term only emerge after the book's publication?

I turned to Bard ( see why here) for the answer, which also explains why this term emerged only in the post-internet world. This is what it wrote, and I have to admit being somewhat amused by the somewhat pushy CTA at the end and if the "I" is meant to be the AI itself:

The term "ideal customer profile" (ICP) was first used in the early 2000s by marketing and sales professionals. It was originally used to describe a hypothetical company that would be the perfect fit for a particular product or service. However, the term has since been adopted by businesses of all sizes and industries.

 

There are a few reasons why businesses started referring to ICPs. First, it became clear that not all customers are created equal. Some customers are more likely to be profitable than others. By understanding the characteristics of their ideal customers, businesses can focus their marketing and sales efforts on the most likely to convert.

 

Second, the rise of the internet made it easier for businesses to collect data about their customers. This data can be used to create detailed ICPs that take into account factors such as company size, industry, location, and pain points.
Finally, the increasing complexity of the marketplace made it more difficult for businesses to compete on price alone. By understanding their ideal customers and their needs, businesses can differentiate themselves from the competition and offer products and services that are truly valuable.

 

Today, ICPs are an essential part of any successful marketing and sales strategy. By creating a detailed ICP, businesses can improve their targeting, messaging, and conversion rates. This can lead to increased sales, improved customer satisfaction, and a stronger bottom line.
Here are some of the benefits of using an ICP:

 

Increased sales: By targeting your marketing and sales efforts to your ideal customers, you can increase your chances of closing deals.

 

Improved customer satisfaction: When you understand the needs of your ideal customers, you can deliver products and services that are more likely to meet their expectations.

 

Stronger bottom line: By improving your sales and customer satisfaction, you can increase your profits.

 

If you're not already using an ICP, I encourage you to start today. It's a simple but powerful tool that can help you take your business to the next level.

Notice that Bard is very bullish on ICPs, even pushy, I'd venture to say. That turns out to be a bit funny when it fails to recognize the acronym for my next query for a more recent business bible.

Clowns you say? 

I also recently read The Hard Thing About Hard Things published in 2014. I don't recollect a single mention of  ICP. Just to be sure, I asked Bard, which shows a kind of split personality when it comes to recalling acronyms. This was its response:

No, the book "The Hard Thing About Hard Things" by Ben Horowitz does not mention ICP. ICP stands for Insane Clown Posse, a hip hop duo from Detroit, Michigan. The book is about the challenges of running a startup company, and ICP is not relevant to that topic.
The book does mention a few other bands, such as The Beatles and The Rolling Stones. However, these bands are mentioned in the context of how they have influenced the music industry. ICP is not mentioned in the book at all.

Knowing that when you work with generative AI you have to try your prompt multiple times, I get it another whirl and wrote out "ideal customer profile" for the query. Now Bard grasped what I was asking and responded:

"The book "The Hard Thing About Hard Things" by Ben Horowitz does not explicitly mention the need for an ideal customer profile. However, the book does talk about the importance of knowing your target market and understanding their needs. This is essentially what an ideal customer profile is.

 

In Chapter 1, Horowitz writes, "The first thing you need to do is figure out who you're building for." He goes on to say that you need to "understand their pain, their goals, and their constraints." This is all information that would be included in an ideal customer profile.
In Chapter 2, Horowitz writes about the importance of "building a product that people actually want." He says that you need to "get feedback from your customers early and often." This is another way of saying that you need to understand your target market and their needs.
Overall, while the book does not explicitly mention the need for an ideal customer profile, it does emphasize the importance of understanding your target market and their needs. This is essentially what an ideal customer profile is.

 

Here are some additional tips for creating an ideal customer profile:
Identify your target market. Who are you trying to sell to? What are their demographics? What are their needs and wants?
Research your target market. What are they reading? What are they talking about online? What are their pain points?
Create a persona. Once you have a good understanding of your target market, create a persona. This is a fictional representation of your ideal customer.

 

Use your ideal customer profile to guide your marketing and sales efforts. Use your persona to create content, develop marketing campaigns, and target your sales efforts.

Related:

A matter of degree

Add a pinch of salt to creative claims for AI

The perils of prompt engineering

Visit WriteWayPro.weebly.com  Like and follow on Facebook and on LinkedIn

 

Did Open AI Open Pandora's Box?

Pandora opening the box that releases  harm into the world. Image at: https://nypl.getarchive.net/media/pandora-opens-the-box-ca3915

OpenAI not only democratized access to AI but popularized it by inviting people to use it for free. Many of us have opened that box, but some of us have been dismayed at the results that speak as if they are objective truth without any accountability for sources of information or explanation for how ChatGPT arrives at its conclusion.

Relying on AI as an objective source of information ignores the fact that it reflects the bias embedded by its human programmers and can reinforce discriminatory effects. The consequences of that can range from biased beauty standards to reinforcing illegal discriminatory practices.  

Now that it just costs $20 a month or can even be available for free at off-peak times, everyone can -- and many are -- make use of ChatGPT instead of doing research in a variety of sources with documentation that offers at least some level of accountability that is essential for explainable AI. 

Read more here:  https://www.linkedin.com/pulse/did-open-ai-pandoras-box-write-way-pro/?trackingId=E3yor3MrHAi2B2HwIdqlVQ%3D%3D

 Related:

An A/B Test of Generative AI

AI' s Got Some Explaining to Do

AI's early attempts at screenwriting

The Pros and Cons of Generative AI

11 Quotes About AI 

AI Informs Personalization for Starbucks

AI Accessibility: The Next Spreadsheet Revolution for Modern Business? 

 

http://uncommoncontent.blogspot.com/2021/01/the-original-selection-of-11-ai-quotes.html

 

Coke' Misappropriates Vermeer With AI

A 3D billboard at Guarulhos International Airport in Sao Paulo is showcases  Coca-Cola's "Masterpiece" campaign by animating Vermeer's  "Girl with a Pearl Earring" as a girl whose life purpose it is to put bottles of Coke into a pipe that leads to the vending machine for the soft drink. 

This is not "real magic TM" as the Coke video declares. It is a travesty of art that reduces a masterpiece to a Disney-style short film. 

I can just picture the thought process behind this with a discussion of the creative team that went like this:

Rich: We need something everyone recognizes as a masterpiece!

Josh: But it has to not be under copyright!

Rich: Right! 

Dave: How about Mona Lisa?

Rich Maybe, but can we find something else?

Dave: Vermeer!

Rich: What?

Dave:  ChatGPT says one of his paintings is really famous because there was a movie about it.

Josh: Oh, yeah, "Girl With a Pearl Earring." So long as that's not under copyright, it should be good. 

Rich: Yeah, any woman in a famous piece of art acting as a waitress to get you to buy Coke should work.  

High fives all around.

And so you have it. The enigmatic subject of Vermeer's masterpiece is reduced to Disney-heroine-as-serving girl to allow Coke to appropriate high culture to sell you brown-colored carbonated water filled with ingredients that are detrimental to your health.

I'm fine with a brand promoting itself and with showing off 3D effects and other high tech. But it should have gone the route of creating its own character for this rather than stealing art that is not copyright protected.

Related: 

MAJOR MARKETING MISSTEPS FROM ADIDAS, M&M'S AND COKE

How chatbots have evolved

 

The origins of the chatbot

The proliferation of chatbots over the last decade may give the impression that they are only a product of the internet. In truth, though, the roots go all the way back to 1966 when Joseph Weizenbaum a German computer scientist and Professor at Massachusetts Institute of Technology developed a program he called ELIZA.

The all caps make it look like an acronym, but ELIZA doesn’t stand for anything. Instead, as explained in the original Stanford article about it: “Its name was chosen to emphasize that it may be incrementally improved by its users, since its language abilities may be continually improved by a ‘teacher.’”

The reference there was to the character of Eliza in George Bernard Shaw’s Pygmalion (more likely recognized by people today as the character in the musical version My Fair Lady). Eliza was hoping to convince others that she was something she was not -- a well-bred lady. Likewise, the program was designed to come across as a human therapist and convince users “that they were having a conversation with a real human being.”

While ELIZA definitely counts as the first chatterbot, the term was only born decades later. In 1994

Michael Maudlin invented a program he named Julia and called the function of a chattering robot “ChatterBot,” and the term soon got shortened to chatbot. 

Chatbots now
While users enjoyed their conversations with those early chatbots, most of us would not mistake them for actual people. But today’s chatbots are a different story. 

They’re able to carry on much more natural-sounding conversations thanks to the application of machine learning, artificial intelligence , and natural language processing. Adding in ML and AI enables them to learn by identifying data patterns and then to apply their knowledge to answer questions and carry out tasks without any human intervention. 

Their greater functionality translates into far more use by businesses and their customers. Today businesses use bots for a range of communication needs, ranging from customer service to product suggestion, scheduling, and various forms of marketing designed to engage the audience.

But the biggest area of growth for chatbots may be in sales. In Chatbots: Vendor Opportunities & Market Forecasts 2020-2024, Juniper Research anticipates consumer retail spend over chatbots will hit $142 billion by 2024, quite a jump from the $2.8 billion we had in 2019.

Juniper also predicts that by 2024 more than half of retail chatbot interactions will go through automatically and that “80% of global consumer spend over chatbots will be attributable to discrete chatbots” that are used through a mobile app rather than a browser. 

On that basis, the report “urges retailers to implement chatbots as part of a wider omnichannel retail strategy in order to maximise their presence on a number of key retail channels.”

Read more in  Choose Your Chatbot Wisely

Diversity produces better quality for AI

Artificial Intelligence (AI) is no longer just a projection into future uses but a part of business practices. Machine learning (ML) is a tool used by businesses for predictive modeling that is used in an array of industries, from healthcare to finance to security.

The question that businesses have to address is: Are we being careful to not misuse AI by having it reinforce human biases in the training data?

To get insight into the various factors that play into that assurance, Martine Bertrand, Lead AI at Samasource in Montreal shared her thoughts. Bertrand holds a Ph.D. in physics and has applied her scientific rigor to ML and AI.

The Source of Bias

Bertrand concurs with what other experts have pointed out: “The model doesn’t choose to have a bias,” but rather she said it: “learns from the data it is exposed to.” Consequently a data set that is biased toward a certain category, class, gender, or color of skin will likely produce an inaccurate model.

We saw several examples of such biased models in Can AI Have Biases? Bertrand referred to one of the instances, that of Amazon’s Rekognition. It came under fire over a year ago when Joy Buolamnwini focused her research on its effects.

Buolamnwini found that while Rekognition did have 100% accuracy in recognizing light-skinned males and 98.7% accuracy even for darker males, the accuracy dropped to 92.9% for women with light skin and just 68.6% accuracy for darker-skinned women

Despite the demand for its removal from law enforcement agencies, the software remained in use. Bertrand finds that outrageous because of the potential danger inherent in relying on biased outcomes in that context.

Read more in  Why Diversity is Essential for Quality Data to Train AI

Ethical concerns raised by AI

Depositphotos

Nearly every day, we hear about new advances in AI that enable new ways to monitor activities and people, transforming many processes in our day to day life.

RELATED: BENTHAM, HOBBES, AND THE ETHICS OF ARTIFICIAL INTELLIGENCE

What we may then hear every other day is how AI can exacerbate racial and gender bias and pose a threat to privacy, job security, and economic well being. It could possibly even spark a war in the view of Elon Musk.

AI-powered  facial recognition raises concerns over privacy and bias

As explained in Facial Recognition Concerns: Microsoft's Six Ethical Principles, “The widespread use of Artificial Intelligence-powered facial recognition technology can lead to some new intrusions into people’s privacy.”

Given the ability to capture people’s image and identify them on public streets in the name of security, people are rightfully concerned that they will lose their ability to maintain any privacy. That extends to environments at school and work, as detailed in the article.

A  2018  New York Times article raised another concern with the headline, “Facial Recognition Is Accurate, if You’re a White Guy.” The problem is this:

“The darker the skin, the more errors arise — up to nearly 35 percent for images of darker skinned women, according to a new study that breaks fresh ground by measuring how the technology works on people of different races and gender.”

The source of these figures is Joy Buolamwini, a researcher at the MIT Media Lab, and the founder of theAlgorithmic Justice League (AJL). She has devoted herself to uncover how biases seep into AI and so skew results for facial recognition.

See her TED Talk in this video:

This year, Buolamnwini published the findings of her research with Inioluwa Deborah Raji from the University of Toronto, in  Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products.

According to that study Amazon's Rekognition software also messed up on those who fall out of the white man category. It misidentified women as men almost one out of fives times, according to the study. In addition, it incorrectly identified darker-skinned women as men 31 percent of the time, it says.

Read more in 

Our Brave New World : Why the Advance of AI Raises Ethical Concerns

11 Takes on AI

Takeaway: The advance of AI is inevitable, and what that translates into for humanity is not altogether clear. Some believe we can look forward to a great future, while others think it means we are on the path to being supplanted by our robotic overlords. The third perspective is one that is aware of risks but considers them to be manageable.

We hear a lot about AI and its transformative potential. What that means for the future of humanity, however, is not altogether clear. Some futurists believe life will be improved, while others think it is under serious threat. There’s also a spectrum of positions in the middle. Here’s a range of takes from 11 experts.

Here's the first 

That is the first sentence in Yudkowsy’s 2002 report entitled “Artificial Intelligence as a Positive and Negative Factor in Global Risk” for the Machine Intelligence Research Institute (MIRI). While the term AI wasn’t bandied about nearly as much then as it is now, there still remains a problem of a lack of understanding on the capabilities and limits of the technology. In fact, in the past couple of years, there’s been more of a push to make AI not just understandable, but explainable.

And we'll jump to the last one: “There is no reason and no way that a human mind can keep up with an artificial Intelligent machine by 2035.” – Gray Scott

This quote is not mistyped, though it deviates from the way you will see it anywhere else online because it always appears as “There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” Here’s the story. Based on how far back it appears in digital sources, it was likely said in 2015. However, I could not pin it down to any particular context even after hours of search through texts and videos from that period. So I contacted Scott himself to ask for the source. He admitted, “I do not recall when the first time was that I said this or where it was.” But he did recall his wording: “The quote has always been wrong. It should read ‘artificial Intelligent.’”

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To read the other nine, click on 11 Quotes About AI That'll Make You Think

eBook on AutoML

 AI can enable marketers to deliver on those expectations because it can anticipate not just what customers would want to hear about but when and how they’d want to receive that communication. “With AI, such personalization can now be achieved with previously unimaginable precision and at vast scale,” insists H. James Wilson and Paul R. Daugherty. The company that does that for music is Pandora. Delivering personalized music selections involves “billions of data points that are tracked across dozens of systems, including media servers, device clients, and ad servers.” To keep all that running smoothly, it needs to detect and address anomalies as they occur. It’s a tall order that is answered with an ML system with the capability to do just that in near real-time, something Pandora will also draw on in assuring its ads are performing as expected.

Read more in my eBook: 2019: the year AutoML takes off

AI: Inception to Present

pic from https://upload.wikimedia.org/wikipedia/commons/5/5f/Kanangra_winter_wonderland.jpg

Takeaway: AI has a surprisingly long history, marked by periods of optimism and support followed by disenchantment. Now that we're at a new high point, we appear poised for the inevitable third round of AI winter. But perhaps this round will be different.

Today we have all kinds of “smart” devices, many of which can even be activated by voice alone and offer intelligent responses to our queries. This kind of cutting-edge technology may make us consider AI to be a product of the 21st century. But it actually has much earlier roots, going all the way back to the middle of the 20th century.

AI Roots

It may be said that Alan Turing’s ideas for computational thinking lay the foundation for AI. John McCarthy, Professor of Computer Science, Stanford University, gives credit to Turing for presenting the concept in a 1947 lecture. Certainly, it is something Turing thought about, for his written work includes a 1950 essay that explores the question, “Can machines think?” This is what gave rise to the famous Turing test. (To learn more, check out Thinking Machines: The Artificial Intelligence Debate.)

Even earlier though, in 1945, Vannevar Bush set out a vision of futuristic technology in an Atlantic Magazine article entitled “As We May Think.” Among the wonders he predicted was a machine able to rapidly process data to bring up people with specific characteristics or find images requested.

Emergence

Thorough as they were in their explanations, none of these visionary thinkers employed the term “artificial intelligence.” That only emerged in 1955 to represent the new field of research that was to be explored. It appeared in the title of “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.” The conference itself took place in the summer of 1956.

As they were poised at the beginning of the decade of optimism, researchers expressed confidence in the future and thought it would take just a generation for AI to become a reality. There was great support for AI in the U.S. during the 1960s. With the Cold War in full swing, the U.S. didn’t want to fall behind the Russians on the technology front. MIT benefited, receiving a $2.2 million grant from DARPA to explore machine-aided cognition in 1963.

Progress continued with funding for a range of AI programs, including, MIT’s SHRDLU, David Marr’s theories of machine vision, Marvin Minsky’s frame theory, the Prolog language, and the development of expert systems. That level of support for AI came to an end by the mid-1970s, though.

And now winter is coming

Read more to learn about AI winters and whether we may be entering into the third one now in A Brief History of AI

The Dark Side of Technological Progress

Takeaway: Media consumption is evolving alongside technological advances like artificial intelligence (AI). While we generally embrace progress, we have to also be aware of the downsides of some forms of technology. There is a dark side to AI’s capability when it is used in promoting fake news.

The dark side of technology was among the topics explored at NYC Media Lab’s second Machines + Media conference, which was sponsored and hosted by Bloomberg at its global headquarters in the city on May 15. Though some of the sessions were more about looking at what tech is currently available to media, even those brought up the shadow of manipulation and misinformation.

Read more in With Great Progress Comes Great Responsibility

The Personal Touch in Marketing

Digital platforms open up new opportunities to reach people wherever they are on multiple devices and to pick up reams of data to feed into marketing campaigns. But with thousands of forgettable, repetitive ads coming at people every day, the challenge is not merely to blend into the background noise, but to offer relevant and personalized communication.

The second Adweek Executive Lab, which took place on Tuesday in New York and was sponsored by Tapad, included several discussions about using data to achieve personalization.

Read more in The Challenges of Drawing on Digital Data for Customized Communication

AI Applied to Healthcare Marketing

DeepIntent's CEO and co-founder Chris Paquette came in with a background in healthcare, having worked as a data scientist for Memorial Sloan Kettering, using AI to find patterns predictive of patient outcomes. Prior to that he worked at a search company. DeepIntent's approach, is built on a combination of the two fields, as he explained in an interview.
Read more in 
Finding the Audiences for Healthcare Marketing

AI-Powered Shop Windows

The Shop Window Opens a Portal of Possibilities

Many of us are window shoppers but online purchasers. That may be one of the factors in the widely reported decline of retail stores. To flip the situation around, it may be time to transform the shop window from static tableaux into AI-powered interactive displays that extract value from data.

This is the proposition offered by Outernets, a New York-based platform which enables window displays to transforms storefronts into responsive, personalized ad experiences.  I spoke with CEO and founder Omer Golan about what store windows have been, and what they could be in the future.

Read more in The Shop Window Opens a Portal of Possibilities